Overview

Dataset statistics

Number of variables21
Number of observations13580
Missing cells13256
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory168.0 B

Variable types

Categorical8
Numeric13

Alerts

Suburb has a high cardinality: 314 distinct valuesHigh cardinality
Address has a high cardinality: 13378 distinct valuesHigh cardinality
SellerG has a high cardinality: 268 distinct valuesHigh cardinality
Date has a high cardinality: 58 distinct valuesHigh cardinality
Rooms is highly overall correlated with Price and 3 other fieldsHigh correlation
Price is highly overall correlated with Rooms and 2 other fieldsHigh correlation
Distance is highly overall correlated with CouncilAreaHigh correlation
Postcode is highly overall correlated with Lattitude and 3 other fieldsHigh correlation
Bedroom2 is highly overall correlated with Rooms and 3 other fieldsHigh correlation
Bathroom is highly overall correlated with Rooms and 2 other fieldsHigh correlation
BuildingArea is highly overall correlated with Rooms and 3 other fieldsHigh correlation
Lattitude is highly overall correlated with Postcode and 1 other fieldsHigh correlation
Longtitude is highly overall correlated with Postcode and 2 other fieldsHigh correlation
CouncilArea is highly overall correlated with Distance and 4 other fieldsHigh correlation
Regionname is highly overall correlated with Postcode and 2 other fieldsHigh correlation
BuildingArea has 6450 (47.5%) missing valuesMissing
YearBuilt has 5375 (39.6%) missing valuesMissing
CouncilArea has 1369 (10.1%) missing valuesMissing
Landsize is highly skewed (γ1 = 95.23740045)Skewed
BuildingArea is highly skewed (γ1 = 77.69154092)Skewed
Address is uniformly distributedUniform
Car has 1026 (7.6%) zerosZeros
Landsize has 1939 (14.3%) zerosZeros

Reproduction

Analysis started2023-08-24 03:10:04.308766
Analysis finished2023-08-24 03:10:36.065027
Duration31.76 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Suburb
Categorical

Distinct314
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
Reservoir
 
359
Richmond
 
260
Bentleigh East
 
249
Preston
 
239
Brunswick
 
222
Other values (309)
12251 

Length

Max length18
Median length15
Mean length9.7964654
Min length3

Characters and Unicode

Total characters133036
Distinct characters49
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.2%

Sample

1st rowAbbotsford
2nd rowAbbotsford
3rd rowAbbotsford
4th rowAbbotsford
5th rowAbbotsford

Common Values

ValueCountFrequency (%)
Reservoir 359
 
2.6%
Richmond 260
 
1.9%
Bentleigh East 249
 
1.8%
Preston 239
 
1.8%
Brunswick 222
 
1.6%
Essendon 220
 
1.6%
South Yarra 202
 
1.5%
Glen Iris 195
 
1.4%
Hawthorn 191
 
1.4%
Coburg 190
 
1.4%
Other values (304) 11253
82.9%

Length

2023-08-24T08:40:36.159478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east 1042
 
5.6%
north 735
 
3.9%
south 531
 
2.8%
west 509
 
2.7%
brunswick 420
 
2.3%
melbourne 418
 
2.2%
bentleigh 388
 
2.1%
reservoir 359
 
1.9%
brighton 324
 
1.7%
hawthorn 310
 
1.7%
Other values (265) 13616
73.0%

Most occurring characters

ValueCountFrequency (%)
e 12017
 
9.0%
o 11338
 
8.5%
r 11177
 
8.4%
n 9667
 
7.3%
a 8724
 
6.6%
t 8260
 
6.2%
l 7303
 
5.5%
i 6507
 
4.9%
s 6373
 
4.8%
5072
 
3.8%
Other values (39) 46598
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 109304
82.2%
Uppercase Letter 18660
 
14.0%
Space Separator 5072
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12017
11.0%
o 11338
10.4%
r 11177
10.2%
n 9667
8.8%
a 8724
 
8.0%
t 8260
 
7.6%
l 7303
 
6.7%
i 6507
 
6.0%
s 6373
 
5.8%
h 4409
 
4.0%
Other values (15) 23529
21.5%
Uppercase Letter
ValueCountFrequency (%)
B 1957
 
10.5%
E 1632
 
8.7%
M 1491
 
8.0%
S 1439
 
7.7%
H 1408
 
7.5%
C 1249
 
6.7%
P 1221
 
6.5%
N 1185
 
6.4%
W 1017
 
5.5%
A 932
 
5.0%
Other values (13) 5129
27.5%
Space Separator
ValueCountFrequency (%)
5072
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 127964
96.2%
Common 5072
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12017
 
9.4%
o 11338
 
8.9%
r 11177
 
8.7%
n 9667
 
7.6%
a 8724
 
6.8%
t 8260
 
6.5%
l 7303
 
5.7%
i 6507
 
5.1%
s 6373
 
5.0%
h 4409
 
3.4%
Other values (38) 42189
33.0%
Common
ValueCountFrequency (%)
5072
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12017
 
9.0%
o 11338
 
8.5%
r 11177
 
8.4%
n 9667
 
7.3%
a 8724
 
6.6%
t 8260
 
6.2%
l 7303
 
5.5%
i 6507
 
4.9%
s 6373
 
4.8%
5072
 
3.8%
Other values (39) 46598
35.0%

Address
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct13378
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
36 Aberfeldie St
 
3
2 Bruce St
 
3
5 Charles St
 
3
53 William St
 
3
14 Arthur St
 
3
Other values (13373)
13565 

Length

Max length27
Median length25
Mean length13.510457
Min length8

Characters and Unicode

Total characters183472
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13185 ?
Unique (%)97.1%

Sample

1st row85 Turner St
2nd row25 Bloomburg St
3rd row5 Charles St
4th row40 Federation La
5th row55a Park St

Common Values

ValueCountFrequency (%)
36 Aberfeldie St 3
 
< 0.1%
2 Bruce St 3
 
< 0.1%
5 Charles St 3
 
< 0.1%
53 William St 3
 
< 0.1%
14 Arthur St 3
 
< 0.1%
28 Blair St 3
 
< 0.1%
5 Margaret St 3
 
< 0.1%
1/1 Clarendon St 3
 
< 0.1%
13 Robinson St 3
 
< 0.1%
4 Bell St 2
 
< 0.1%
Other values (13368) 13551
99.8%

Length

2023-08-24T08:40:36.362744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st 8052
 
19.7%
rd 2825
 
6.9%
ct 612
 
1.5%
dr 447
 
1.1%
av 321
 
0.8%
gr 311
 
0.8%
3 260
 
0.6%
4 257
 
0.6%
5 251
 
0.6%
7 241
 
0.6%
Other values (7068) 27329
66.8%

Most occurring characters

ValueCountFrequency (%)
27326
 
14.9%
t 12785
 
7.0%
e 9573
 
5.2%
S 9002
 
4.9%
r 8628
 
4.7%
a 8075
 
4.4%
n 7309
 
4.0%
1 7036
 
3.8%
o 6788
 
3.7%
l 6303
 
3.4%
Other values (54) 80647
44.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92167
50.2%
Decimal Number 32266
 
17.6%
Uppercase Letter 27779
 
15.1%
Space Separator 27326
 
14.9%
Other Punctuation 3934
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 12785
13.9%
e 9573
10.4%
r 8628
9.4%
a 8075
8.8%
n 7309
 
7.9%
o 6788
 
7.4%
l 6303
 
6.8%
d 5940
 
6.4%
i 5031
 
5.5%
s 3384
 
3.7%
Other values (16) 18351
19.9%
Uppercase Letter
ValueCountFrequency (%)
S 9002
32.4%
R 3536
 
12.7%
C 2148
 
7.7%
B 1419
 
5.1%
M 1303
 
4.7%
A 1299
 
4.7%
D 1122
 
4.0%
P 1102
 
4.0%
G 1093
 
3.9%
H 926
 
3.3%
Other values (16) 4829
17.4%
Decimal Number
ValueCountFrequency (%)
1 7036
21.8%
2 4894
15.2%
3 3833
11.9%
4 3129
9.7%
5 2756
 
8.5%
6 2445
 
7.6%
7 2218
 
6.9%
0 2146
 
6.7%
8 2020
 
6.3%
9 1789
 
5.5%
Space Separator
ValueCountFrequency (%)
27326
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 3934
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119946
65.4%
Common 63526
34.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 12785
 
10.7%
e 9573
 
8.0%
S 9002
 
7.5%
r 8628
 
7.2%
a 8075
 
6.7%
n 7309
 
6.1%
o 6788
 
5.7%
l 6303
 
5.3%
d 5940
 
5.0%
i 5031
 
4.2%
Other values (42) 40512
33.8%
Common
ValueCountFrequency (%)
27326
43.0%
1 7036
 
11.1%
2 4894
 
7.7%
/ 3934
 
6.2%
3 3833
 
6.0%
4 3129
 
4.9%
5 2756
 
4.3%
6 2445
 
3.8%
7 2218
 
3.5%
0 2146
 
3.4%
Other values (2) 3809
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 183472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27326
 
14.9%
t 12785
 
7.0%
e 9573
 
5.2%
S 9002
 
4.9%
r 8628
 
4.7%
a 8075
 
4.4%
n 7309
 
4.0%
1 7036
 
3.8%
o 6788
 
3.7%
l 6303
 
3.4%
Other values (54) 80647
44.0%

Rooms
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9379971
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:36.691038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.95574794
Coefficient of variation (CV)0.32530596
Kurtosis0.79406799
Mean2.9379971
Median Absolute Deviation (MAD)1
Skewness0.37647803
Sum39898
Variance0.91345412
MonotonicityNot monotonic
2023-08-24T08:40:36.831773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 5881
43.3%
2 3648
26.9%
4 2688
19.8%
1 681
 
5.0%
5 596
 
4.4%
6 67
 
0.5%
7 10
 
0.1%
8 8
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
1 681
 
5.0%
2 3648
26.9%
3 5881
43.3%
4 2688
19.8%
5 596
 
4.4%
6 67
 
0.5%
7 10
 
0.1%
8 8
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 8
 
0.1%
7 10
 
0.1%
6 67
 
0.5%
5 596
 
4.4%
4 2688
19.8%
3 5881
43.3%
2 3648
26.9%
1 681
 
5.0%

Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
h
9449 
u
3017 
t
1114 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13580
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowh
2nd rowh
3rd rowh
4th rowh
5th rowh

Common Values

ValueCountFrequency (%)
h 9449
69.6%
u 3017
 
22.2%
t 1114
 
8.2%

Length

2023-08-24T08:40:36.988994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-24T08:40:37.192780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
h 9449
69.6%
u 3017
 
22.2%
t 1114
 
8.2%

Most occurring characters

ValueCountFrequency (%)
h 9449
69.6%
u 3017
 
22.2%
t 1114
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13580
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 9449
69.6%
u 3017
 
22.2%
t 1114
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 13580
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 9449
69.6%
u 3017
 
22.2%
t 1114
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 9449
69.6%
u 3017
 
22.2%
t 1114
 
8.2%

Price
Real number (ℝ)

Distinct2204
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1075684.1
Minimum85000
Maximum9000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:37.349018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum85000
5-th percentile405000
Q1650000
median903000
Q31330000
95-th percentile2290050
Maximum9000000
Range8915000
Interquartile range (IQR)680000

Descriptive statistics

Standard deviation639310.72
Coefficient of variation (CV)0.59432945
Kurtosis9.8743389
Mean1075684.1
Median Absolute Deviation (MAD)313000
Skewness2.2396243
Sum1.460779 × 1010
Variance4.087182 × 1011
MonotonicityNot monotonic
2023-08-24T08:40:37.552813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100000 113
 
0.8%
1300000 109
 
0.8%
650000 109
 
0.8%
800000 109
 
0.8%
600000 104
 
0.8%
1000000 97
 
0.7%
1200000 97
 
0.7%
900000 95
 
0.7%
700000 91
 
0.7%
1400000 89
 
0.7%
Other values (2194) 12567
92.5%
ValueCountFrequency (%)
85000 1
 
< 0.1%
131000 1
 
< 0.1%
145000 2
< 0.1%
160000 1
 
< 0.1%
170000 2
< 0.1%
185000 2
< 0.1%
190000 1
 
< 0.1%
200000 2
< 0.1%
210000 4
< 0.1%
215000 1
 
< 0.1%
ValueCountFrequency (%)
9000000 1
< 0.1%
8000000 1
< 0.1%
7650000 1
< 0.1%
6500000 1
< 0.1%
6400000 1
< 0.1%
6250000 1
< 0.1%
5800000 1
< 0.1%
5700000 1
< 0.1%
5600000 1
< 0.1%
5525000 1
< 0.1%

Method
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
S
9022 
SP
1703 
PI
1564 
VB
1199 
SA
 
92

Length

Max length2
Median length1
Mean length1.3356406
Min length1

Characters and Unicode

Total characters18138
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowSP
4th rowPI
5th rowVB

Common Values

ValueCountFrequency (%)
S 9022
66.4%
SP 1703
 
12.5%
PI 1564
 
11.5%
VB 1199
 
8.8%
SA 92
 
0.7%

Length

2023-08-24T08:40:37.740884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-24T08:40:37.913266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
s 9022
66.4%
sp 1703
 
12.5%
pi 1564
 
11.5%
vb 1199
 
8.8%
sa 92
 
0.7%

Most occurring characters

ValueCountFrequency (%)
S 10817
59.6%
P 3267
 
18.0%
I 1564
 
8.6%
V 1199
 
6.6%
B 1199
 
6.6%
A 92
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18138
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 10817
59.6%
P 3267
 
18.0%
I 1564
 
8.6%
V 1199
 
6.6%
B 1199
 
6.6%
A 92
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 18138
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 10817
59.6%
P 3267
 
18.0%
I 1564
 
8.6%
V 1199
 
6.6%
B 1199
 
6.6%
A 92
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18138
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 10817
59.6%
P 3267
 
18.0%
I 1564
 
8.6%
V 1199
 
6.6%
B 1199
 
6.6%
A 92
 
0.5%

SellerG
Categorical

Distinct268
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
Nelson
1565 
Jellis
1316 
hockingstuart
1167 
Barry
1011 
Ray
 
701
Other values (263)
7820 

Length

Max length23
Median length19
Mean length6.4025037
Min length1

Characters and Unicode

Total characters86946
Distinct characters57
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)0.6%

Sample

1st rowBiggin
2nd rowBiggin
3rd rowBiggin
4th rowBiggin
5th rowNelson

Common Values

ValueCountFrequency (%)
Nelson 1565
 
11.5%
Jellis 1316
 
9.7%
hockingstuart 1167
 
8.6%
Barry 1011
 
7.4%
Ray 701
 
5.2%
Marshall 659
 
4.9%
Buxton 632
 
4.7%
Biggin 393
 
2.9%
Brad 342
 
2.5%
Fletchers 301
 
2.2%
Other values (258) 5493
40.4%

Length

2023-08-24T08:40:38.100771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nelson 1565
 
11.5%
jellis 1316
 
9.7%
hockingstuart 1167
 
8.6%
barry 1011
 
7.4%
ray 701
 
5.2%
marshall 659
 
4.9%
buxton 632
 
4.7%
biggin 393
 
2.9%
brad 342
 
2.5%
fletchers 301
 
2.2%
Other values (255) 5493
40.4%

Most occurring characters

ValueCountFrequency (%)
l 7767
 
8.9%
a 7196
 
8.3%
s 6910
 
7.9%
r 6662
 
7.7%
e 6538
 
7.5%
o 5706
 
6.6%
n 5268
 
6.1%
i 4936
 
5.7%
t 4061
 
4.7%
h 2851
 
3.3%
Other values (47) 29051
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72731
83.7%
Uppercase Letter 13923
 
16.0%
Other Punctuation 178
 
0.2%
Decimal Number 114
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 7767
10.7%
a 7196
9.9%
s 6910
9.5%
r 6662
9.2%
e 6538
9.0%
o 5706
 
7.8%
n 5268
 
7.2%
i 4936
 
6.8%
t 4061
 
5.6%
h 2851
 
3.9%
Other values (15) 14836
20.4%
Uppercase Letter
ValueCountFrequency (%)
B 2654
19.1%
N 1852
13.3%
J 1617
11.6%
R 1303
9.4%
M 1294
9.3%
G 674
 
4.8%
W 584
 
4.2%
H 478
 
3.4%
S 413
 
3.0%
C 403
 
2.9%
Other values (15) 2651
19.0%
Other Punctuation
ValueCountFrequency (%)
' 106
59.6%
& 32
 
18.0%
. 29
 
16.3%
/ 9
 
5.1%
@ 2
 
1.1%
Decimal Number
ValueCountFrequency (%)
2 57
50.0%
1 57
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86654
99.7%
Common 292
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 7767
 
9.0%
a 7196
 
8.3%
s 6910
 
8.0%
r 6662
 
7.7%
e 6538
 
7.5%
o 5706
 
6.6%
n 5268
 
6.1%
i 4936
 
5.7%
t 4061
 
4.7%
h 2851
 
3.3%
Other values (40) 28759
33.2%
Common
ValueCountFrequency (%)
' 106
36.3%
2 57
19.5%
1 57
19.5%
& 32
 
11.0%
. 29
 
9.9%
/ 9
 
3.1%
@ 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86946
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 7767
 
8.9%
a 7196
 
8.3%
s 6910
 
7.9%
r 6662
 
7.7%
e 6538
 
7.5%
o 5706
 
6.6%
n 5268
 
6.1%
i 4936
 
5.7%
t 4061
 
4.7%
h 2851
 
3.3%
Other values (47) 29051
33.4%

Date
Categorical

Distinct58
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
27/05/2017
 
473
3/06/2017
 
395
12/08/2017
 
387
17/06/2017
 
374
27/11/2016
 
362
Other values (53)
11589 

Length

Max length10
Median length10
Mean length9.7248159
Min length9

Characters and Unicode

Total characters132063
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3/12/2016
2nd row4/02/2016
3rd row4/03/2017
4th row4/03/2017
5th row4/06/2016

Common Values

ValueCountFrequency (%)
27/05/2017 473
 
3.5%
3/06/2017 395
 
2.9%
12/08/2017 387
 
2.8%
17/06/2017 374
 
2.8%
27/11/2016 362
 
2.7%
29/07/2017 341
 
2.5%
4/03/2017 337
 
2.5%
25/02/2017 333
 
2.5%
24/06/2017 329
 
2.4%
10/12/2016 319
 
2.3%
Other values (48) 9930
73.1%

Length

2023-08-24T08:40:38.279181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27/05/2017 473
 
3.5%
3/06/2017 395
 
2.9%
12/08/2017 387
 
2.8%
17/06/2017 374
 
2.8%
27/11/2016 362
 
2.7%
29/07/2017 341
 
2.5%
4/03/2017 337
 
2.5%
25/02/2017 333
 
2.5%
24/06/2017 329
 
2.4%
10/12/2016 319
 
2.3%
Other values (48) 9930
73.1%

Most occurring characters

ValueCountFrequency (%)
/ 27160
20.6%
0 26427
20.0%
1 22198
16.8%
2 21040
15.9%
7 11327
8.6%
6 9451
 
7.2%
8 3453
 
2.6%
9 3075
 
2.3%
5 2954
 
2.2%
3 2690
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104903
79.4%
Other Punctuation 27160
 
20.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26427
25.2%
1 22198
21.2%
2 21040
20.1%
7 11327
10.8%
6 9451
 
9.0%
8 3453
 
3.3%
9 3075
 
2.9%
5 2954
 
2.8%
3 2690
 
2.6%
4 2288
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/ 27160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 132063
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 27160
20.6%
0 26427
20.0%
1 22198
16.8%
2 21040
15.9%
7 11327
8.6%
6 9451
 
7.2%
8 3453
 
2.6%
9 3075
 
2.3%
5 2954
 
2.2%
3 2690
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132063
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 27160
20.6%
0 26427
20.0%
1 22198
16.8%
2 21040
15.9%
7 11327
8.6%
6 9451
 
7.2%
8 3453
 
2.6%
9 3075
 
2.3%
5 2954
 
2.2%
3 2690
 
2.0%

Distance
Real number (ℝ)

Distinct202
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.137776
Minimum0
Maximum48.1
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:38.460788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6
Q16.1
median9.2
Q313
95-th percentile20.6
Maximum48.1
Range48.1
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation5.8687249
Coefficient of variation (CV)0.57889668
Kurtosis5.2600011
Mean10.137776
Median Absolute Deviation (MAD)3.35
Skewness1.6769371
Sum137671
Variance34.441932
MonotonicityNot monotonic
2023-08-24T08:40:38.679540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.2 739
 
5.4%
9.2 367
 
2.7%
13.9 324
 
2.4%
7.8 306
 
2.3%
4.6 263
 
1.9%
13 252
 
1.9%
8 248
 
1.8%
5.2 248
 
1.8%
13.8 237
 
1.7%
2.6 235
 
1.7%
Other values (192) 10361
76.3%
ValueCountFrequency (%)
0 6
 
< 0.1%
0.7 8
 
0.1%
1.2 33
 
0.2%
1.3 5
 
< 0.1%
1.5 17
 
0.1%
1.6 106
0.8%
1.8 72
0.5%
1.9 48
0.4%
2 13
 
0.1%
2.1 78
0.6%
ValueCountFrequency (%)
48.1 1
 
< 0.1%
47.4 1
 
< 0.1%
47.3 3
 
< 0.1%
45.9 9
0.1%
45.2 1
 
< 0.1%
44.2 1
 
< 0.1%
43.3 1
 
< 0.1%
41 6
< 0.1%
39.8 1
 
< 0.1%
39 2
 
< 0.1%

Postcode
Real number (ℝ)

Distinct198
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3105.3019
Minimum3000
Maximum3977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:38.898869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3000
5-th percentile3013
Q13044
median3084
Q33148
95-th percentile3204
Maximum3977
Range977
Interquartile range (IQR)104

Descriptive statistics

Standard deviation90.676964
Coefficient of variation (CV)0.029200692
Kurtosis29.156868
Mean3105.3019
Median Absolute Deviation (MAD)50
Skewness4.0761522
Sum42170000
Variance8222.3118
MonotonicityNot monotonic
2023-08-24T08:40:39.102142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3073 359
 
2.6%
3020 306
 
2.3%
3121 292
 
2.2%
3040 290
 
2.1%
3046 284
 
2.1%
3165 249
 
1.8%
3058 246
 
1.8%
3163 245
 
1.8%
3012 242
 
1.8%
3072 239
 
1.8%
Other values (188) 10828
79.7%
ValueCountFrequency (%)
3000 46
 
0.3%
3002 22
 
0.2%
3003 31
 
0.2%
3006 41
 
0.3%
3008 3
 
< 0.1%
3011 194
1.4%
3012 242
1.8%
3013 164
1.2%
3015 188
1.4%
3016 126
0.9%
ValueCountFrequency (%)
3977 8
0.1%
3976 4
 
< 0.1%
3910 6
< 0.1%
3810 3
 
< 0.1%
3809 1
 
< 0.1%
3808 1
 
< 0.1%
3807 2
 
< 0.1%
3806 13
0.1%
3805 7
0.1%
3803 5
 
< 0.1%

Bedroom2
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9147275
Minimum0
Maximum20
Zeros16
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:39.290361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.96592106
Coefficient of variation (CV)0.33139326
Kurtosis8.0749638
Mean2.9147275
Median Absolute Deviation (MAD)1
Skewness0.77408221
Sum39582
Variance0.9330035
MonotonicityNot monotonic
2023-08-24T08:40:39.446620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 5896
43.4%
2 3737
27.5%
4 2601
19.2%
1 691
 
5.1%
5 556
 
4.1%
6 63
 
0.5%
0 16
 
0.1%
7 10
 
0.1%
8 5
 
< 0.1%
9 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 16
 
0.1%
1 691
 
5.1%
2 3737
27.5%
3 5896
43.4%
4 2601
19.2%
5 556
 
4.1%
6 63
 
0.5%
7 10
 
0.1%
8 5
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
10 1
 
< 0.1%
9 3
 
< 0.1%
8 5
 
< 0.1%
7 10
 
0.1%
6 63
 
0.5%
5 556
 
4.1%
4 2601
19.2%
3 5896
43.4%
2 3737
27.5%

Bathroom
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5342415
Minimum0
Maximum8
Zeros34
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:39.602863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.69171172
Coefficient of variation (CV)0.4508493
Kurtosis3.5949731
Mean1.5342415
Median Absolute Deviation (MAD)0
Skewness1.377406
Sum20835
Variance0.47846511
MonotonicityNot monotonic
2023-08-24T08:40:39.869044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 7512
55.3%
2 4974
36.6%
3 917
 
6.8%
4 106
 
0.8%
0 34
 
0.3%
5 28
 
0.2%
6 5
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 34
 
0.3%
1 7512
55.3%
2 4974
36.6%
3 917
 
6.8%
4 106
 
0.8%
5 28
 
0.2%
6 5
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 2
 
< 0.1%
6 5
 
< 0.1%
5 28
 
0.2%
4 106
 
0.8%
3 917
 
6.8%
2 4974
36.6%
1 7512
55.3%
0 34
 
0.3%

Car
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing62
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.6100755
Minimum0
Maximum10
Zeros1026
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:40.041483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.96263352
Coefficient of variation (CV)0.597881
Kurtosis5.1931828
Mean1.6100755
Median Absolute Deviation (MAD)1
Skewness1.3696759
Sum21765
Variance0.92666329
MonotonicityNot monotonic
2023-08-24T08:40:40.202547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 5591
41.2%
1 5509
40.6%
0 1026
 
7.6%
3 748
 
5.5%
4 506
 
3.7%
5 63
 
0.5%
6 54
 
0.4%
8 9
 
0.1%
7 8
 
0.1%
10 3
 
< 0.1%
(Missing) 62
 
0.5%
ValueCountFrequency (%)
0 1026
 
7.6%
1 5509
40.6%
2 5591
41.2%
3 748
 
5.5%
4 506
 
3.7%
5 63
 
0.5%
6 54
 
0.4%
7 8
 
0.1%
8 9
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 3
 
< 0.1%
9 1
 
< 0.1%
8 9
 
0.1%
7 8
 
0.1%
6 54
 
0.4%
5 63
 
0.5%
4 506
 
3.7%
3 748
 
5.5%
2 5591
41.2%
1 5509
40.6%

Landsize
Real number (ℝ)

SKEWED  ZEROS 

Distinct1448
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean558.41613
Minimum0
Maximum433014
Zeros1939
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:40.390068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1177
median440
Q3651
95-th percentile995
Maximum433014
Range433014
Interquartile range (IQR)474

Descriptive statistics

Standard deviation3990.6692
Coefficient of variation (CV)7.1464076
Kurtosis10180.347
Mean558.41613
Median Absolute Deviation (MAD)236
Skewness95.2374
Sum7583291
Variance15925441
MonotonicityNot monotonic
2023-08-24T08:40:40.593192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1939
 
14.3%
650 103
 
0.8%
697 71
 
0.5%
700 48
 
0.4%
585 47
 
0.3%
534 42
 
0.3%
590 39
 
0.3%
649 36
 
0.3%
696 36
 
0.3%
604 35
 
0.3%
Other values (1438) 11184
82.4%
ValueCountFrequency (%)
0 1939
14.3%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
10 1
 
< 0.1%
14 1
 
< 0.1%
15 2
 
< 0.1%
17 1
 
< 0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
433014 1
< 0.1%
76000 1
< 0.1%
75100 1
< 0.1%
44500 1
< 0.1%
41400 1
< 0.1%
40468 1
< 0.1%
38490 1
< 0.1%
37000 2
< 0.1%
21715 1
< 0.1%
21700 1
< 0.1%

BuildingArea
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct602
Distinct (%)8.4%
Missing6450
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean151.96765
Minimum0
Maximum44515
Zeros17
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:40.796915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51
Q193
median126
Q3174
95-th percentile294
Maximum44515
Range44515
Interquartile range (IQR)81

Descriptive statistics

Standard deviation541.01454
Coefficient of variation (CV)3.5600639
Kurtosis6347.8022
Mean151.96765
Median Absolute Deviation (MAD)39
Skewness77.691541
Sum1083529.3
Variance292696.73
MonotonicityNot monotonic
2023-08-24T08:40:41.016212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 114
 
0.8%
110 89
 
0.7%
100 88
 
0.6%
130 84
 
0.6%
115 77
 
0.6%
150 74
 
0.5%
104 66
 
0.5%
90 65
 
0.5%
140 64
 
0.5%
125 63
 
0.5%
Other values (592) 6346
46.7%
(Missing) 6450
47.5%
ValueCountFrequency (%)
0 17
0.1%
1 11
0.1%
2 16
0.1%
3 20
0.1%
4 4
 
< 0.1%
5 3
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
44515 1
< 0.1%
6791 1
< 0.1%
3558 1
< 0.1%
3112 1
< 0.1%
1561 1
< 0.1%
1143 1
< 0.1%
1041 1
< 0.1%
1022 1
< 0.1%
934 1
< 0.1%
826.8367 1
< 0.1%

YearBuilt
Real number (ℝ)

Distinct144
Distinct (%)1.8%
Missing5375
Missing (%)39.6%
Infinite0
Infinite (%)0.0%
Mean1964.6842
Minimum1196
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:41.219340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1196
5-th percentile1900
Q11940
median1970
Q31999
95-th percentile2012
Maximum2018
Range822
Interquartile range (IQR)59

Descriptive statistics

Standard deviation37.273762
Coefficient of variation (CV)0.018971885
Kurtosis21.226032
Mean1964.6842
Median Absolute Deviation (MAD)30
Skewness-1.5412788
Sum16120234
Variance1389.3334
MonotonicityNot monotonic
2023-08-24T08:40:41.422465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970 866
 
6.4%
1960 725
 
5.3%
1950 580
 
4.3%
1900 341
 
2.5%
1980 338
 
2.5%
2000 300
 
2.2%
1920 280
 
2.1%
1930 274
 
2.0%
1910 240
 
1.8%
1940 238
 
1.8%
Other values (134) 4023
29.6%
(Missing) 5375
39.6%
ValueCountFrequency (%)
1196 1
 
< 0.1%
1830 1
 
< 0.1%
1850 4
< 0.1%
1854 1
 
< 0.1%
1856 1
 
< 0.1%
1857 1
 
< 0.1%
1860 3
< 0.1%
1862 1
 
< 0.1%
1863 3
< 0.1%
1868 1
 
< 0.1%
ValueCountFrequency (%)
2018 1
 
< 0.1%
2017 18
 
0.1%
2016 58
 
0.4%
2015 65
 
0.5%
2014 100
0.7%
2013 136
1.0%
2012 197
1.5%
2011 131
1.0%
2010 176
1.3%
2009 110
0.8%

CouncilArea
Categorical

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)0.3%
Missing1369
Missing (%)10.1%
Memory size106.2 KiB
Moreland
1163 
Boroondara
1160 
Moonee Valley
997 
Darebin
934 
Glen Eira
848 
Other values (28)
7109 

Length

Max length17
Median length12
Mean length9.0692818
Min length4

Characters and Unicode

Total characters110745
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowYarra
2nd rowYarra
3rd rowYarra
4th rowYarra
5th rowYarra

Common Values

ValueCountFrequency (%)
Moreland 1163
 
8.6%
Boroondara 1160
 
8.5%
Moonee Valley 997
 
7.3%
Darebin 934
 
6.9%
Glen Eira 848
 
6.2%
Stonnington 719
 
5.3%
Maribyrnong 692
 
5.1%
Yarra 647
 
4.8%
Port Phillip 628
 
4.6%
Banyule 594
 
4.4%
Other values (23) 3829
28.2%
(Missing) 1369
 
10.1%

Length

2023-08-24T08:40:41.609976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
moreland 1163
 
7.7%
boroondara 1160
 
7.6%
moonee 997
 
6.6%
valley 997
 
6.6%
darebin 934
 
6.1%
glen 848
 
5.6%
eira 848
 
5.6%
stonnington 719
 
4.7%
maribyrnong 692
 
4.6%
yarra 665
 
4.4%
Other values (28) 6172
40.6%

Most occurring characters

ValueCountFrequency (%)
n 13579
12.3%
o 11999
 
10.8%
a 11840
 
10.7%
r 10051
 
9.1%
e 9358
 
8.5%
l 6633
 
6.0%
i 6440
 
5.8%
M 4120
 
3.7%
y 3330
 
3.0%
B 3101
 
2.8%
Other values (29) 30294
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92566
83.6%
Uppercase Letter 15195
 
13.7%
Space Separator 2984
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 13579
14.7%
o 11999
13.0%
a 11840
12.8%
r 10051
10.9%
e 9358
10.1%
l 6633
7.2%
i 6440
7.0%
y 3330
 
3.6%
t 3082
 
3.3%
d 3045
 
3.3%
Other values (11) 13209
14.3%
Uppercase Letter
ValueCountFrequency (%)
M 4120
27.1%
B 3101
20.4%
P 1256
 
8.3%
V 997
 
6.6%
D 986
 
6.5%
G 900
 
5.9%
E 848
 
5.6%
S 719
 
4.7%
Y 665
 
4.4%
H 598
 
3.9%
Other values (7) 1005
 
6.6%
Space Separator
ValueCountFrequency (%)
2984
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 107761
97.3%
Common 2984
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 13579
12.6%
o 11999
11.1%
a 11840
11.0%
r 10051
 
9.3%
e 9358
 
8.7%
l 6633
 
6.2%
i 6440
 
6.0%
M 4120
 
3.8%
y 3330
 
3.1%
B 3101
 
2.9%
Other values (28) 27310
25.3%
Common
ValueCountFrequency (%)
2984
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 13579
12.3%
o 11999
 
10.8%
a 11840
 
10.7%
r 10051
 
9.1%
e 9358
 
8.5%
l 6633
 
6.0%
i 6440
 
5.8%
M 4120
 
3.7%
y 3330
 
3.0%
B 3101
 
2.8%
Other values (29) 30294
27.4%

Lattitude
Real number (ℝ)

Distinct6503
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-37.809203
Minimum-38.18255
Maximum-37.40853
Zeros0
Zeros (%)0.0%
Negative13580
Negative (%)100.0%
Memory size106.2 KiB
2023-08-24T08:40:41.813769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-38.18255
5-th percentile-37.9348
Q1-37.856822
median-37.802355
Q3-37.7564
95-th percentile-37.698938
Maximum-37.40853
Range0.77402
Interquartile range (IQR)0.1004225

Descriptive statistics

Standard deviation0.079259823
Coefficient of variation (CV)-0.0020963103
Kurtosis1.5732527
Mean-37.809203
Median Absolute Deviation (MAD)0.050455
Skewness-0.42669493
Sum-513448.97
Variance0.0062821195
MonotonicityNot monotonic
2023-08-24T08:40:42.050162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-37.8361 21
 
0.2%
-37.8424 16
 
0.1%
-37.7969 16
 
0.1%
-37.7609 14
 
0.1%
-37.8573 13
 
0.1%
-37.8414 13
 
0.1%
-37.8161 13
 
0.1%
-37.7634 13
 
0.1%
-37.7679 13
 
0.1%
-37.8198 13
 
0.1%
Other values (6493) 13435
98.9%
ValueCountFrequency (%)
-38.18255 1
< 0.1%
-38.17488 1
< 0.1%
-38.16802 1
< 0.1%
-38.16762 1
< 0.1%
-38.16624 1
< 0.1%
-38.16492 1
< 0.1%
-38.16483 1
< 0.1%
-38.16475 1
< 0.1%
-38.16457 2
< 0.1%
-38.16439 1
< 0.1%
ValueCountFrequency (%)
-37.40853 1
< 0.1%
-37.45392 1
< 0.1%
-37.45709 1
< 0.1%
-37.48381 1
< 0.1%
-37.48701 1
< 0.1%
-37.49175 1
< 0.1%
-37.49642 1
< 0.1%
-37.49674 1
< 0.1%
-37.50087 1
< 0.1%
-37.50624 1
< 0.1%

Longtitude
Real number (ℝ)

Distinct7063
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.99522
Minimum144.43181
Maximum145.52635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:42.285202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum144.43181
5-th percentile144.83579
Q1144.9296
median145.0001
Q3145.05831
95-th percentile145.15363
Maximum145.52635
Range1.09454
Interquartile range (IQR)0.128705

Descriptive statistics

Standard deviation0.10391556
Coefficient of variation (CV)0.00071668269
Kurtosis1.7586156
Mean144.99522
Median Absolute Deviation (MAD)0.063415
Skewness-0.2109909
Sum1969035
Variance0.010798444
MonotonicityNot monotonic
2023-08-24T08:40:42.504840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144.9966 17
 
0.1%
145.0104 15
 
0.1%
144.985 14
 
0.1%
145.0001 13
 
0.1%
144.991 13
 
0.1%
145.021 12
 
0.1%
145.0116 12
 
0.1%
145.0043 12
 
0.1%
145.0243 12
 
0.1%
144.9873 12
 
0.1%
Other values (7053) 13448
99.0%
ValueCountFrequency (%)
144.43181 1
< 0.1%
144.48571 1
< 0.1%
144.54237 1
< 0.1%
144.54532 1
< 0.1%
144.55106 1
< 0.1%
144.55666 1
< 0.1%
144.55784 1
< 0.1%
144.55833 1
< 0.1%
144.55857 1
< 0.1%
144.55888 1
< 0.1%
ValueCountFrequency (%)
145.52635 1
< 0.1%
145.48273 1
< 0.1%
145.47052 1
< 0.1%
145.45376 1
< 0.1%
145.4453 1
< 0.1%
145.43698 1
< 0.1%
145.43003 1
< 0.1%
145.42554 1
< 0.1%
145.41288 1
< 0.1%
145.41081 1
< 0.1%

Regionname
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
Southern Metropolitan
4695 
Northern Metropolitan
3890 
Western Metropolitan
2948 
Eastern Metropolitan
1471 
South-Eastern Metropolitan
 
450
Other values (3)
 
126

Length

Max length26
Median length21
Mean length20.796907
Min length16

Characters and Unicode

Total characters282422
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorthern Metropolitan
2nd rowNorthern Metropolitan
3rd rowNorthern Metropolitan
4th rowNorthern Metropolitan
5th rowNorthern Metropolitan

Common Values

ValueCountFrequency (%)
Southern Metropolitan 4695
34.6%
Northern Metropolitan 3890
28.6%
Western Metropolitan 2948
21.7%
Eastern Metropolitan 1471
 
10.8%
South-Eastern Metropolitan 450
 
3.3%
Eastern Victoria 53
 
0.4%
Northern Victoria 41
 
0.3%
Western Victoria 32
 
0.2%

Length

2023-08-24T08:40:42.818120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-24T08:40:43.038846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
metropolitan 13454
49.5%
southern 4695
 
17.3%
northern 3931
 
14.5%
western 2980
 
11.0%
eastern 1524
 
5.6%
south-eastern 450
 
1.7%
victoria 126
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t 41064
14.5%
o 36110
12.8%
r 31091
11.0%
e 30014
10.6%
n 27034
9.6%
a 15554
 
5.5%
i 13706
 
4.9%
13580
 
4.8%
p 13454
 
4.8%
M 13454
 
4.8%
Other values (11) 47361
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 240782
85.3%
Uppercase Letter 27610
 
9.8%
Space Separator 13580
 
4.8%
Dash Punctuation 450
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 41064
17.1%
o 36110
15.0%
r 31091
12.9%
e 30014
12.5%
n 27034
11.2%
a 15554
 
6.5%
i 13706
 
5.7%
p 13454
 
5.6%
l 13454
 
5.6%
h 9076
 
3.8%
Other values (3) 10225
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
M 13454
48.7%
S 5145
 
18.6%
N 3931
 
14.2%
W 2980
 
10.8%
E 1974
 
7.1%
V 126
 
0.5%
Space Separator
ValueCountFrequency (%)
13580
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 450
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 268392
95.0%
Common 14030
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 41064
15.3%
o 36110
13.5%
r 31091
11.6%
e 30014
11.2%
n 27034
10.1%
a 15554
 
5.8%
i 13706
 
5.1%
p 13454
 
5.0%
M 13454
 
5.0%
l 13454
 
5.0%
Other values (9) 33457
12.5%
Common
ValueCountFrequency (%)
13580
96.8%
- 450
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 282422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 41064
14.5%
o 36110
12.8%
r 31091
11.0%
e 30014
10.6%
n 27034
9.6%
a 15554
 
5.5%
i 13706
 
4.9%
13580
 
4.8%
p 13454
 
4.8%
M 13454
 
4.8%
Other values (11) 47361
16.8%

Propertycount
Real number (ℝ)

Distinct311
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7454.4174
Minimum249
Maximum21650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 KiB
2023-08-24T08:40:43.273319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum249
5-th percentile2185
Q14380
median6555
Q310331
95-th percentile14949
Maximum21650
Range21401
Interquartile range (IQR)5951

Descriptive statistics

Standard deviation4378.5818
Coefficient of variation (CV)0.58738082
Kurtosis1.21782
Mean7454.4174
Median Absolute Deviation (MAD)2695.5
Skewness1.0693393
Sum1.0123099 × 108
Variance19171978
MonotonicityNot monotonic
2023-08-24T08:40:43.476448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21650 359
 
2.6%
8870 298
 
2.2%
14949 260
 
1.9%
10969 249
 
1.8%
14577 239
 
1.8%
11918 222
 
1.6%
9264 220
 
1.6%
14887 202
 
1.5%
10412 195
 
1.4%
11308 191
 
1.4%
Other values (301) 11145
82.1%
ValueCountFrequency (%)
249 1
 
< 0.1%
389 6
 
< 0.1%
394 2
 
< 0.1%
438 7
 
0.1%
457 2
 
< 0.1%
534 6
 
< 0.1%
538 1
 
< 0.1%
570 2
 
< 0.1%
588 26
0.2%
608 9
 
0.1%
ValueCountFrequency (%)
21650 359
2.6%
17496 46
 
0.3%
17384 3
 
< 0.1%
17093 13
 
0.1%
17055 24
 
0.2%
16166 50
 
0.4%
15542 13
 
0.1%
15510 47
 
0.3%
15321 45
 
0.3%
14949 260
1.9%

Interactions

2023-08-24T08:40:32.932706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:05.930083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:08.062482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:10.268077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:12.410920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:14.729251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:17.175305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:19.406189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:21.790626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:23.908430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:26.241523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:28.190249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:30.593553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:33.057711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:06.055843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:08.228578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:10.392663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:12.585718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:14.870139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:17.349463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:19.531897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:21.929169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:24.096969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:26.367345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:28.377722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:30.766176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:33.183894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:06.180865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:08.349067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:10.564489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:12.757974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:15.045434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:17.538416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:19.689414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:22.070085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:24.221962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:26.524676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:28.565224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:30.892967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:33.324636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:06.358979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:08.508009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:10.721643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:12.897436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:15.195955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:17.726495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:19.847251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:22.242496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:24.394588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:26.649673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:28.847364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:31.050168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:33.480887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:06.531722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:08.697602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:10.865106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:13.214063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:15.330757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:17.885010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:20.020840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:22.388242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:24.566463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:26.790296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:29.052894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:31.177046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:33.637129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:06.674407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:08.887118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:11.006173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:13.407496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:15.536649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:18.044954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:20.264413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:22.531248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:24.754444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:26.916012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:29.209870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:31.364863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:33.794099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:06.844872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:09.073429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:11.163747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:13.560491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:15.727682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:18.218075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:20.449396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:22.718868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:25.086668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:27.056636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:29.382383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:31.567984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:33.951035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:07.019201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:09.341509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:11.339768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:13.733249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:15.932830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:18.384561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:20.625750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:22.891774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:25.269335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:27.215986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:29.585521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:31.709131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:34.060394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:07.209999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:09.499052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:11.513145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:13.922308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:16.119071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:18.534529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:20.766426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:23.017257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:25.425588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:27.421090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:29.746979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:31.834497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:34.247910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:07.387613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:09.625754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:11.669816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:14.080142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:16.262347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:18.681518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:21.057437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:23.189273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:25.597466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:27.546093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:29.888723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:32.022711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:34.419777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:07.529949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:09.798908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:11.843328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:14.221230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:16.436542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:18.868560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:21.272496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:23.361070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:25.769338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:27.690566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:30.029351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:32.195077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:34.607267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:07.750701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:09.954704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:12.051856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:14.395706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:16.657152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:19.063923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:21.476037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:23.564684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:25.958037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:27.891750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:30.249785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:32.384970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:34.795664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:07.888785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:10.092866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:12.220921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:14.571759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:16.848742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:19.215460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:21.649956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:23.752184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:26.115569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:28.018237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:30.452916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-08-24T08:40:32.751026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-08-24T08:40:43.679561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
RoomsPriceDistancePostcodeBedroom2BathroomCarLandsizeBuildingAreaYearBuiltLattitudeLongtitudePropertycountTypeMethodDateCouncilAreaRegionname
Rooms1.0000.5400.3510.0290.9600.5870.4760.4860.775-0.0720.0370.133-0.0780.4570.0720.0630.1430.095
Price0.5401.000-0.1300.2300.5240.4270.2880.3270.631-0.368-0.2600.262-0.0110.3040.0630.0480.1700.141
Distance0.351-0.1301.0000.2100.3580.1570.3470.4170.2870.229-0.0100.312-0.1420.1890.0680.1600.6780.448
Postcode0.0290.2300.2101.0000.0350.1240.0570.0610.079-0.030-0.5880.6800.1330.0970.0660.0730.6720.544
Bedroom20.9600.5240.3580.0351.0000.5800.4770.4800.762-0.0580.0390.136-0.0770.4190.0510.0870.1650.101
Bathroom0.5870.4270.1570.1240.5801.0000.3720.2120.6510.202-0.0810.144-0.0350.2220.0630.0470.0930.072
Car0.4760.2880.3470.0570.4770.3721.0000.4070.4720.1070.0080.132-0.0430.2740.0270.0570.1240.075
Landsize0.4860.3270.4170.0610.4800.2120.4071.0000.471-0.1290.0570.198-0.0740.0000.0140.0000.1100.082
BuildingArea0.7750.6310.2870.0790.7620.6510.4720.4711.0000.003-0.0210.141-0.0820.0000.0000.2040.0380.132
YearBuilt-0.072-0.3680.229-0.030-0.0580.2020.107-0.1290.0031.0000.067-0.005-0.0050.2230.0210.0810.1660.108
Lattitude0.037-0.260-0.010-0.5880.039-0.0810.0080.057-0.0210.0671.000-0.356-0.0320.1530.0570.1110.6960.449
Longtitude0.1330.2620.3120.6800.1360.1440.1320.1980.141-0.005-0.3561.0000.0830.1390.0700.1200.7680.574
Propertycount-0.078-0.011-0.1420.133-0.077-0.035-0.043-0.074-0.082-0.005-0.0320.0831.0000.1310.0420.0630.4330.212
Type0.4570.3040.1890.0970.4190.2220.2740.0000.0000.2230.1530.1390.1311.0000.0640.1250.2450.153
Method0.0720.0630.0680.0660.0510.0630.0270.0140.0000.0210.0570.0700.0420.0641.0000.0520.1150.080
Date0.0630.0480.1600.0730.0870.0470.0570.0000.2040.0810.1110.1200.0630.1250.0521.0000.0840.121
CouncilArea0.1430.1700.6780.6720.1650.0930.1240.1100.0380.1660.6960.7680.4330.2450.1150.0841.0000.804
Regionname0.0950.1410.4480.5440.1010.0720.0750.0820.1320.1080.4490.5740.2120.1530.0800.1210.8041.000

Missing values

2023-08-24T08:40:35.077425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-24T08:40:35.563864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-24T08:40:35.908655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SuburbAddressRoomsTypePriceMethodSellerGDateDistancePostcodeBedroom2BathroomCarLandsizeBuildingAreaYearBuiltCouncilAreaLattitudeLongtitudeRegionnamePropertycount
0Abbotsford85 Turner St2h1480000.0SBiggin3/12/20162.53067.02.01.01.0202.0NaNNaNYarra-37.7996144.9984Northern Metropolitan4019.0
1Abbotsford25 Bloomburg St2h1035000.0SBiggin4/02/20162.53067.02.01.00.0156.079.01900.0Yarra-37.8079144.9934Northern Metropolitan4019.0
2Abbotsford5 Charles St3h1465000.0SPBiggin4/03/20172.53067.03.02.00.0134.0150.01900.0Yarra-37.8093144.9944Northern Metropolitan4019.0
3Abbotsford40 Federation La3h850000.0PIBiggin4/03/20172.53067.03.02.01.094.0NaNNaNYarra-37.7969144.9969Northern Metropolitan4019.0
4Abbotsford55a Park St4h1600000.0VBNelson4/06/20162.53067.03.01.02.0120.0142.02014.0Yarra-37.8072144.9941Northern Metropolitan4019.0
5Abbotsford129 Charles St2h941000.0SJellis7/05/20162.53067.02.01.00.0181.0NaNNaNYarra-37.8041144.9953Northern Metropolitan4019.0
6Abbotsford124 Yarra St3h1876000.0SNelson7/05/20162.53067.04.02.00.0245.0210.01910.0Yarra-37.8024144.9993Northern Metropolitan4019.0
7Abbotsford98 Charles St2h1636000.0SNelson8/10/20162.53067.02.01.02.0256.0107.01890.0Yarra-37.8060144.9954Northern Metropolitan4019.0
8Abbotsford6/241 Nicholson St1u300000.0SBiggin8/10/20162.53067.01.01.01.00.0NaNNaNYarra-37.8008144.9973Northern Metropolitan4019.0
9Abbotsford10 Valiant St2h1097000.0SBiggin8/10/20162.53067.03.01.02.0220.075.01900.0Yarra-37.8010144.9989Northern Metropolitan4019.0
SuburbAddressRoomsTypePriceMethodSellerGDateDistancePostcodeBedroom2BathroomCarLandsizeBuildingAreaYearBuiltCouncilAreaLattitudeLongtitudeRegionnamePropertycount
13570Wantirna South34 Fewster Dr3h970000.0SBarry26/08/201714.73152.03.02.02.0674.0NaNNaNNaN-37.88360145.22805Eastern Metropolitan7082.0
13571Wantirna South15 Mara Cl4h1330000.0SBarry26/08/201714.73152.04.02.02.0717.0191.01980.0NaN-37.86887145.22116Eastern Metropolitan7082.0
13572Watsonia76 Kenmare St2h650000.0PIMorrison26/08/201714.53087.02.01.01.0210.079.02006.0NaN-37.70657145.07878Northern Metropolitan2329.0
13573Werribee5 Nuragi Ct4h635000.0Shockingstuart26/08/201714.73030.04.02.01.0662.0172.01980.0NaN-37.89327144.64789Western Metropolitan16166.0
13574Westmeadows9 Black St3h582000.0SRed26/08/201716.53049.03.02.02.0256.0NaNNaNNaN-37.67917144.89390Northern Metropolitan2474.0
13575Wheelers Hill12 Strada Cr4h1245000.0SBarry26/08/201716.73150.04.02.02.0652.0NaN1981.0NaN-37.90562145.16761South-Eastern Metropolitan7392.0
13576Williamstown77 Merrett Dr3h1031000.0SPWilliams26/08/20176.83016.03.02.02.0333.0133.01995.0NaN-37.85927144.87904Western Metropolitan6380.0
13577Williamstown83 Power St3h1170000.0SRaine26/08/20176.83016.03.02.04.0436.0NaN1997.0NaN-37.85274144.88738Western Metropolitan6380.0
13578Williamstown96 Verdon St4h2500000.0PISweeney26/08/20176.83016.04.01.05.0866.0157.01920.0NaN-37.85908144.89299Western Metropolitan6380.0
13579Yarraville6 Agnes St4h1285000.0SPVillage26/08/20176.33013.04.01.01.0362.0112.01920.0NaN-37.81188144.88449Western Metropolitan6543.0